Skill UI

A comprehensive, end-to-end methodology designed for software engineers and AI agents to safely and systematically integrate machine learning models into existing non-ML codebases. It guides users through critical phases, including problem framing, data readiness, architectural decoupling (via APIs/services), baseline model implementation, and final MLOps pipeline setup.

This comprehensive API provides a robust solution for automated document workflows. Users can perform format conversions (PDF, DOCX, XLSX, etc.), extract structured data (text, tables), perform OCR on scanned documents, redact sensitive information (PII), add watermarks, and apply digital signatures. Suitable for building automated, secure document processing pipelines.

This optimizer guides the acceleration of complex, multi-stage processes, such as CI/CD pipelines, large-scale refactoring, or data validation. It instructs users to decompose large tasks into independent execution lanes, build a dependency graph, batch read/check operations, and manage writes with strict isolation to maximize speed without sacrificing correctness. Ideal for optimizing development workflows and testing cycles.

A comprehensive guide covering best practices for creating videos using Remotion and React. Learn how to incorporate advanced features such as 3D rendering (Three.js), complex animations, audio synchronization, data charting, subtitles, and sophisticated scene transitions. This skill covers 29 domain-specific rules for professional video pipelines.

Manages the creation and orchestration of complex, multi-step workflows. Supports two powerful methods: persistent MCP templates for human-gated, resumable pipelines, or native JavaScript scripts for advanced, programmatic agent fan-out. Ideal for complex business logic such as auditing, research, or multi-stage data processing.

A comprehensive tool for importing structured data from various sources (CSV, JSON, Google Sheets) into connected platforms like CRMs, email marketing systems, or custom data stores. It manages the entire import pipeline, including automated field mapping, rigorous data validation, deduplication against existing records, and critical compliance verification (GDPR, CCPA). Features a mandatory execution gate for risk management.

Provides comprehensive guidelines for achieving scientific reproducibility in academic research, particularly for control systems, automation, and pattern recognition. It covers fixing environment dependencies, data handling, random seeds, and process pipelines, helping authors withstand rigorous peer review questioning and build verifiable research artifacts.

A comprehensive guide for authors submitting networking research to CoNEXT. It outlines best practices for achieving technical reproducibility, focusing on pinning traces, configurations, environments, and pipelines. Learn the critical timeline for optional ACM badging and how to maintain an honest data-availability posture for robust academic review.

A comprehensive guide detailing the multi-cycle, rolling model for EDBT research submissions. It covers the entire review pipeline, from initial program committee evaluation and author-feedback exchange, through the accept/revise/reject decision, and critically, the in-cycle revise-and-resubmit stage. The guide also contrasts EDBT's open-access, three-cycle cadence with other top-tier database conferences (e.g., SIGMOD, VLDB).

A comprehensive guide for structuring academic empirical research projects to ensure the full reproducibility of findings. This methodology covers standard folder layouts, master scripting, data provenance documentation, and best practices required for passing rigorous peer review scrutiny, thereby preventing data loss and calculation errors during revisions.

This skill acts as the central orchestrator for preparing Environmental Science & Technology (ES&T) manuscripts. It guides users through the complex submission pipeline by determining the optimal article type (e.g., Research Article, Critical Review, Policy Analysis) and the current stage of writing, ensuring the content maintains clear environmental significance. It routes users to specific sub-skills like data analysis, literature positioning, and writing style.

A comprehensive framework for ensuring rigorous scientific reproducibility in systems papers. It details how to establish a full provenance ledger (tracking hardware, software commits, and workloads), manage performance variance through repeated runs and statistical reporting, and implement automated pipelines so that all reported figures can be regenerated from raw data.